Advanced Multi-Objective Optimization for Laser Cladding of H13 Die Steel with CFOA

Area of Science:

• Materials Science and Engineering
• Manufacturing Processes
• Computational Intelligence

Background:

• Laser cladding quality is heavily influenced by complex, nonlinear process parameter interactions.
• Existing research often overlooks the optimization of process parameters alongside surface properties.
• A gap exists in understanding the integrated relationship between parameters, surface characteristics, and optimization strategies.

Purpose of the Study:

• To develop a novel parameter modeling and optimization approach for laser cladding using the Catch Fish Optimization Algorithm (CFOA).
• To systematically analyze the effects of laser power, scanning speed, and powder feeding rate on H13 die steel cladding quality.
• To bridge the research gap concerning the optimization of laser cladding process parameters and surface properties.

Main Methods:

• Employed an orthogonal experimental design to establish a regression model for predicting cladding quality metrics (dilution rate, microhardness, aspect ratio).
• Utilized the Analytic Hierarchy Process (AHP) to convert the multi-objective optimization problem into a single-objective framework.
• Applied the Catch Fish Optimization Algorithm (CFOA) for parameter modeling and optimization.

Main Results:

• Identified an optimal parameter combination: 1628.19 W laser power, 9.9 mm/s scanning speed, and 14.73 g/min powder feeding rate.
• Achieved significant experimental improvements: 19.71% enhancement in dilution rate, 3.37% in microhardness, and 28.66% in aspect ratio.
• Demonstrated CFOA's superior global search capabilities and precision compared to conventional optimization methods.

Conclusions:

• The proposed CFOA-based methodology offers an innovative and effective approach for optimizing laser cladding processes.
• This study provides practical solutions for H13 die steel repair and surface property enhancement.
• Highlights the versatility and robustness of metaheuristic algorithms in advancing complex manufacturing process optimization.